T-cells sense their environment by means of T-cell receptors (TCRs) on their surface. A T-cell expresses about 30,000 copies of a unique (clonotypic) TCR, whose ligands are complexes composed of a peptide bound to an MHC molecule (pMHC). In vivo, TCR ligands are expressed on the surface of antigen-presenting cells (APCs). In the thymus a variety of professional APCs will subject immature T-cells (or thymocytes) to a "double test" by displaying a wide range of pMHC complexes, with peptides derived from household proteins (self-peptides). The stochastic nature of gene rearrangements implies that some TCRs will not be able to recognise a self-pMHC ligand (TCRs that are not functional) and that others will recognise it far too well, and thus would give rise to mature T-cells with the potential to generate autoimmune responses. Thus, the need for a double test that will check the functionality of a thymocyte(positive selection) and its state of tolerance, so that it
does not recognise self-pMHC complexes with high affinities (negative selection).
This thymic selection process only allows 2-5% of thymocytes to become mature T-cells.We have made use of mathematical modelling to address the following issues: (1) the thymic affinity threshold hypothesis proposed by Palmer and Naeher (Nature Reviews Immunology, 2009) and (2) time is precious for T-cells, so what do TCRs sense (i) equilibrium properties or (ii) stochastic events. We have made use of data from Palmer's group (The Journal of Experimental Medicine, 2007) to compare the equilibrium versus the stochastic hypotheses. Our results indicate that the stochastic hypothesis ties in better with the existing immunological evidence and provides support to the affinity threshold hypothesis. The stochastic model has also been applied to recent two-dimensional binding data by Huang et al. (Nature 2010) and sheds light into 2d versus 3d binding kinetics and T-cell responses.

In recent years, we have used software engineering tools to develop reactive models to simulate and analyze the development of organs. The modeled systems embody highly complex and dynamic processes, by which a set of precursor stem cells proliferate, differentiate and move, to form a functioning tissue. Three organs from diverse evolutionary organisms have been thus modeled: the mouse pancreas, the C. elegans gonad, and partial rodent brain development. Analysis and execution of the models provided dynamic representation of the development, anticipated known experimental results and proposed novel testable predictions. In my talk, I will l discuss challenges, goals and achievement in this direction in science.

Event-Driven Automation in Laser-Scanning Microscopy Applied to Live Cell Imaging

Dr Jakub Wenus, Systems Biology, Hamilton Institute, NUIM

Microscopy of living cells is heavily employed in biomedicine to understand the mechanisms of disease progression and to develop novel pharmaceuticals. In particular, confocal microscopy which relies on laser-based excitation of fluorescent cellular biomarkers is frequently used for understanding molecular actions of therapeutic drugs to abnormal cells. However, prolonged exposure to highly energetic laser radiation often leads to light induced cell death before any spontaneous effects can occur – an effect known as phototoxicity. To address this problem we have developed an automated live-cell imaging system ALISSA which employs online image processing and analysis to automatically detect biological events and then trigger appropriate changes in the image acquisition settings. This way we minimize the photo-toxicity, obtain higher quality of the imaging data and minimize direct user involvement by introducing more automation to the whole experimental process. So far, ALISSA has been used in studies on cancer cells and neurons at the Royal College of Surgeons in Ireland and it is currently under development aimed towards applications in commercial high content screening systems.

Contemporary models of speech recognition by humans and machines are difficult to reconcile with many properties of spoken language. Pronunciation variation, robustness to acoustic interference, categorical perception, and lexical access are among the (many) phenomena the "standard framework" fails to explain. This presentation describes a new theoretical formulation, using hierarchical oscillatory networks (Hi- O Nets), that relates auditory speech processing with other sensory (e.g., vision) and cognitive (e.g., memory) data streams. Within the Hi-O framework, signal-parsing and pattern-matching are crucial stages in going from sound to meaning. They depend on the structured interaction of oscillatory neural activity across a broad range of time constants characteristic of speech (20-2000 ms). A multi-time-scale, hierarchical oscillatory framework can account for many phenomena in spoken language, including (1) the ability to understand speech in background noise and other forms of acoustic interference, (2) the effect of sentential and semantic context on speech intelligibility, and (3) the perceptual invariance of highly variable and dynamic acoustic signals. Hi-O Nets will be illustrated and discussed with reference to both classic and more recent perceptual studies. Flyer.

For complex cellular networks, limited mechanistic knowledge,conflicting hypotheses, and relatively scarce experimental data hamper the development of mathematical models as systems analysis tools. The talk focuses on two approaches for dealing with this combination of complexity and uncertainty. They combine theory development and applications to specific biological examples. Firstly, network reaction stoichiometries are relatively well-characterized and therefore suitable starting points for pathway analysis. It allows one to investigate the space of a (metabolic) networkos feasible states. Applications are becoming possible for genomescale networks, and they range from investigating the effects of network perturbations to predicting cellular control features. Moreover, recent theory extensions connect the approach to systems dynamics, for instance, to identify key mechanisms in cellular decision processes.
Secondly, and more mechanistically, we propose to cast hypotheses into a library of dynamic mathematical models, evaluate these against experimental observations, and design pivotal experiments to discriminate between alternatives. For TOR signaling in yeast, this strategy identified key control mechanisms that are quantitatively consistent with all available experimental data, and systematic extension of the
approach to larger networks is a current challenge. Overall, the importance of network structures seems to outweigh the fine tuning of parameters. Structure-oriented analysis of biological systems, thus, provides challenging theory problems as well as broad perspectives for uncovering the organization and functionality of cellular networks. Flyer

Feb 10, 2010

Combining Pharmacology and Mutational Dynamics to Understand and Combat Drug Resistance in HIV

The cure for HIV remains to be found, even after 25 years of research. The use of highly active antiretroviral therapy (HAART) has led to a dramatic decline in morbidity associated with the infection. However, the virus develops drug resistance, thereby eliminating treatment options and putting the patient in risk of death. Up to now, the mechanisms of resistance development are poorly understood.
A population of species (like HIV) can respond to a novel threat (drug treatment) by generating offspring with an adapted phenotype (drug resistance).
During the first month of HAART, the concentration of virus in the blood is reduced by at least five orders of magnitude. This reduction of viral abundance is, however, not paralleled by a reduction in the probability to develop
resistance.
Stable, ongoing replication of HIV in compartments, which are not reflected by clinical measurements (HIV concentration in the blood), might explain this inconsistency. The reasons for insufficient drug penetration, and consequently
-inhibition, can be elucidated by studying the pharmacology of antiviral drugs.
While the two aspects, pharmacology and viral dynamics are often studied separately, we aim at combining them. To this end, we developed mathematical modeling approaches that enable to simultaneously consider the
pharmacology of drugs, their distinct mechanism of action, viral dynamics and the ability of the virus to adapt to the pharmacological challenge. The mathematical models are constructed in a way that allows the use of various in
vitro and in vivo data for parameterization. Consequently, the models can be used to study reasons for resistance emergence. Flyer.

Global sensitivity analysis of ODEs & a statistical study of mice sperm?
The talk is split into two parts. The first part contains the main results of my thesis, where I focused on ODEs subject to uncertain or variable parameter and initial values. Assuming that the initial uncertainty is characterized by a known probability density function (pdf), the final uncertainty can be characterized by solving a first-order PDE that describes the evolution of the pdf in time. The main outcome of this work is a numerical method for adaptive density propagation---adaptive both in temporal & spatial discretization---which can be used for global sensitivity analysis of ODEs.
In the second part of the talk I present a statistical study from a project in collaboration with the developmental genetics group at the MPI for molecular genetics. The group studies molecular mechanisms during spermatogenesis that lead to non-Mendelian inheritance in mice. The current belief in genetics is that between sperm cells, developing in a common syncytium, gene products are exchanged via intercellular bridges. As a result, all sperm cells within one individual are phenotypically equivalent and thus have equal chances of fertilizing the ovum---the basic assumption for Mendelian genetics. In this study we could show that some genes, involved in sperm motility, can escape this mechanism and are actively retained within the cell. The resulting phenotypical difference contradicts the current dogma and provides an explanation for non-Mendelian inheritance.

Dec 9, 2009

A Phylogenetic Hidden Markov Model for Immune Epitope Discovery

Prof. Cathal Seoighe, Dept. of Mathematics, NUI Galway

We describe a phylogenetic model of protein-coding sequence evolution that includes environmental variables. We apply it to a set of viral sequences from individuals with known human leukocyte antigen (HLA) genotype and include parameters to model selective pressures affecting mutations within immunogenic (epitope) regions that facilitate viral evasion of immune responses. We combine this evolutionary model with a hidden Markov model to identify regions of the HIV-1 genome that evolve under immune pressure in the presence of specific HLA class I alleles and may therefore represent potential T cell epitopes. This phylogenetic hidden Markov model (phylo-HMM) provides a probabilistic framework that can be combined with sequence or structural information to enhance epitope prediction.
Flyer.

July 15, 2009

Can't move to the rhythm? Inappropriate neuronal synchrony and oscillations in Parkinson's disease.

The overall objective of the Magill Group’s research is to provide new insights into the functions and mechanisms of neuronal network activity in the basal ganglia, with a focus on elucidating how mutual interactions between these nuclei, as well as inputs from key extrinsic sources like the cerebral cortex and thalamus, orchestrate the activity patterns that are generated therein. Electrophysiological and anatomical techniques are used to dissect the normal and pathological (Parkinsonian) interactions of neurons within these circuits. This multidisciplinary approach, and the use of two complementary in vivo preparations, anaesthetised and freely-moving rodents, enables the group to elucidate the importance and substrates of neuronal network activity at several functional levels. Flyer.

Apoptosis is an important physiological process crucially involved in the development and homeostasis of multi-cellular organisms. Although the major signalling pathways leading from the extrinsic induction to the execution of apoptosis have been unravelled, a detailed mechanistic understanding of the complex underlying network and the signal crosstalk remains elusive. A systems biology approach allows to combine diverse data into mathematical models to perform predictive simulations and testing of quantitative and dynamical hypotheses. The modelling process furthermore reveals theoretical and computational challenges. Flyer.

June 10, 2009

How to understand the cell by breaking it - computational inference of cellular networks from gene perturbation screens

Nowadays, data are recorded with increasing spatio as well as temporal resolution. This calls for new methods to analyze these data sets. Caused by the high spatio as well as temporal resolution of the recorded signals, inference of the causal network structure underlying them becomes feasible. In many applications a detailed analysis of these networks allows deeper insights into the normal functioning or malfunctioning of the system. In Neurology this helps to understand certain diseases like epilepsy or Parkinson's disease.

Novel concepts to analyze multivariate data consisting of both time series as well as point processes will be presented. By means of an application to tremor in Parkinson's disease, the abilities and limitations of these techniques are discussed.

Mar 12, 2009

Value of Pharmacokinetic and Pharmacodynamic Modelling for Tumour Patients

Prof. Charlotte Kloft, Martin-Luther-Universität Halle-Wittenberg

In cancer chemotherapy, despite dose adaptation to body surface area
for classical cytotoxic agents and some novel ‘targeted therapies’, the
degree of interpatient variability in effects is large: Some patients fail
to respond, whereas others experience unacceptable toxicity.
Pharmacokinetic analysis of (sparse) concentration-time data in
oncology have provided useful and sometimes crucial information
during drug development and therapeutic use. However, only few
(population) pharmacodynamic models have been presented, mostly
focussing on myelosuppression as the most common, often dose-
limiting toxicity. Myelosuppression, especially neutropenia makes
patients highly susceptible to pathogens resulting in life-threatening
infections or even death.

This presentation will focus on pharmacokinetic and pharmacodynamic
modelling, especially mechanism-based population models, that may
contribute to more rational drug development and optimal use of these
drugs in tumour patients.

John P. O'Doherty, Trinity College Institute of Neuroscience, Trinity College Dublin, Ireland.

Mar 4, 2009

Analysis of dynamical systems with steep sigmoidal response functions

Prof. Erik Plathe, Norwegian Centre for Integrative Genomics

In models for gene regulation, the activity of a gene is regulated by the concentration
of certain transcription factors. Frequently one assumes that the effect of the
transcription factor (the response function) rises sharply from a low level to a
saturation level around a threshold: i.e. its response function is sigmoidal or perhaps
binary (a step function). Both cases are seen in experiments. If a gene is regulated by
several transcription factors, Boolean-like functions determine their combined effect on
the gene.

Gene regulatory models with step functions can be dealt with by means of a method
devised by Filippov. After briefly sketching the idea behind this method, I will turn to
models with steep sigmoidal response functions. The major problem is to analyse the
behaviour when one or several variables are near a threshold. There the model
becomes discontinuous in the limit when the sigmoids approach step functions.

I derive an equivalent set of equations which behave smoothly in these parts of phase
space and which are much easier to analyse than the original equations. By
investigating the limit when the sigmoids approach step functions, I will show how
singular perturbation theory can be employed to analyse the behaviour of the
model and compute solutions valid in the step function limit. These solutions are
uniform approximations to the solutions for steep sigmoids. The limit solution could
also be seen as an alternative to the Filippov definition of the solution to the same
model with step functions instead of sigmoids. Flyer

Jan 28, 2009

Kinetic Modelling of Metabolism

Prof. David Fell, Oxford Brookes University

I will introduce the principles behind, and different approaches to, the
building of mathematical models of metabolism. Then, focusing on
kinetic models, I will deal with the issues of defining and parameterizing
appropriate rate functions. Applications of kinetic modelling, and the
analysis of kinetic models, will be illustrated with examples from
bacterial threonine metabolism, plant carbohydrate metabolism and the
mitochondrial tricarboxylic acid cycle. Similar approaches can be
applied to the modelling of other cellular processes. Flyer.

Jan 21, 2009

Extracellular Potassium Dynamics and Epileptogenesis

Maxim Bazhenov, University of California, Riverside

Extracellular ion concentrations change as a function of neuronal activity and also
represent important factors influencing the dynamic state of a population of neurons.
In particular, relatively small changes in extracellular potassium concentration mediate
substantial changes in neuronal excitability and intrinsic firing patterns. While
experimental approaches are limited in their ability to shed light on the dynamic
feedback interaction between ion concentration and neural activity, computational
models and dynamic system theory provide powerful tools to study activity-dependent
modulation of intrinsic excitability mediated by extracellular ion concentration
dynamics.
Drawing on results obtained with biophysical network models of the thalamocortical
system, I will discuss the potential role of extracellular potassium concentration
dynamics in the generation of epileptoform activity in neocortical networks. Detailed
bifurcation analysis of a model pyramidal cell revealed a bistability with hysteresis
between two distinct firing modes (tonic firing and slow bursting) for mildly elevated
extracellular potassium. In neocortical network models, this bistability gives rise to
previously unexplained slow alternating epochs of fast runs and slow bursting as
recorded in vivo during neocortical electrographic seizures in cats and in human
patients with the Lennox-Gastaut syndrome. We conclude that extracellular potassium
concentration dynamics may play an important role in the generation of seizures.

Jan 14, 2009

Modelling the synapse: from numbers to networks

Eduardo Mendoza, Ludwig-Maximilian Universität, München.

Recent studies have reinforced the important role of synaptic processes, in
particular in enabling synaptic plasticity, for understanding brain function
as well as neuropsychiatric diseases (e.g. [1], [2]). The talk will first
review the evolution of synapse models from mere numerical constants
('weights') in early connectionist models to the emerging systems
biological view of complex, dynamic networks. It will then discuss how the
study of synaptic networks is contributing not only to understanding
neuropsychiatric diseases but also how it could contribute to bringing
together traditional computational neuroscience and the emerging
systems neurobiology [3,4].
[1] L. Abbott, W.A. Regehr, Synaptic computation (2004), Nature 431.
[2] Kauer, J.A., Malenko, R.C. Synaptic plasticity and addiction (2007), Nature Reviews
Neuroscience, Vol. 8, Nov 2007
[3] N. le Novere, The long journey to a Systems Biology of neuronal function (2007),
BMC Systems Biology I: 28
[4] E. de Schutter, Why Are Computational Neuroscience and Systems Biology so
separate? PLoS Computational Biology (2008), Vol 4, Issue 5

The dynamics of biochemical networks such as regulatory or signalling pathways may entail qualitative changes in its behaviour as the values of their parameters, namely kinetic constants or enzyme concentrations are perturbed. These parameters are in fact responsible of a rich dynamic behaviour which in the form of multistability or oscillations, sustains functionality at the cell level.
From this perspective, it seems worth exploring periodicity, instability or any other qualitative features the system could exhibit for different ranges of parameter values. To that purpose, classical bifurcation techniques could be employed provided that the number of critical parameters remains small. Unfortunately this is not the case for most biochemical networks, where a large number of critical parameters might be involved.
In this seminar I would like to present some ideas and results we are working on to systematically detect and explore the regions in the space of parameters where different complex behaviour might appear. The approach has been built in the formalism of Chemical Reaction Network Theory as developed by Horn and Feinberg.
Interestingly, these regions -with their own characteristic behaviour- turn out to depend on a very small number of parameters closely related to the deficiency of the biochemical network under consideration. As it will be illustrated through some representative examples, the methodology can be also applied to compute the set of all possible reaction network parameters leading to multiple equilibria.

Nov 12, 2008

A systems approach to the modelling of visual hallucinations

Richard Abadi, E.T.S. Walton Visitor

Sep 24, 2008

Spatial mapping of the Earth beneath the ocean - systems of a different kind

Peter Simpkin, IKB Technology

The idea of treating our planet as a system was first mentioned by Renaissance scientists as they looked for new models with which to describe the universe. This process continued through the centuries, until the book Ã¢ÂÂGaia: A New Look at Life on EarthÃ¢ÂÂ by James Lovelock, introduced the public at large to the shocking fact that Earth was a limited resource with bio-systems that reacted to inputs from mankind Ã¢ÂÂ often in unattractive ways.
No group of researchers is more aware of the limited and special nature of the planet than marine biologists and geophysicists. They have known this for many decades and through marine biology and geology have traced the gradual impact of mankind. This talk will give an overview of this area taking the view of a marine geophysicist and instrumentation specialist who over a forty year career has designed, built and applied marine survey equipment. Drawing on experiences in most of our seas and oceans, but most notably of the Eastern Seaboard of North America, Dr Simpkin will describe the problems and successes of spatial mapping the EarthÃ¢ÂÂs oceans. Flyer.

May 14th, 2008

An Integrative Computational Model of Colorectal Carcinogenesis

Dr. Ingeborg M.M. van Leeuwen, University of Dundee, Scotland

As part of the Integrative Biology project, we have formulated a multi-scale model to
describe the processes involved in normal intestinal tissue renewal and colorectal
cancer (CRC) development. At the subcellular level, deterministic continuum models
characterise the behaviour of fundamental biochemical networks (i.e. cell-cycle control
and Wnt signalling) in response to intra- and extra-cellular cues. The outcome of these
models determines the regulation and co-ordination of cellular events (i.e.
proliferation, differentiation, apoptosis, migration and adhesion) within the intestinal
epithelium. Under aberrant conditions, loss of control can cause increased cell division
and/or decreased cell differentiation and death. This can have serious implications for
the maintenance of the integrity of the crypt, as the resulting proliferative excess and
biomechanical stress can lead to crypt deformation, fission and eventual polyp
formation. Our multi-scale approach enables us to investigate the impact of mutations
commonly detected in CRCs, combine highly disparate data-sets, explore possible
interactions between phenomena occurring at different levels of organisation and, in
the future, test anti-cancer drugs on the system as a whole. Flyer.

Apr 16th, 2008

Dark Energy, Vacuum Fluctuations and Microscopic Irreversibility

Prof. Michael C. Mackey, McGill University

Modern ergodic theory of dynamical and semi-dynamical systems, combined with
newer concepts from irreversible thermodynamics, have given new and surprising
insights into the possible origins of irreversible behaviour. In this talk I will outline
these in detail. I further hypothesize that one of the conclusions reached may be
related to a possible connection between vacuum fluctuations (zero point energy) and
the recently discovered dark energy driving the accelerated expansion of the universe.
Flyer.

Optimization is a key methodology in engineering. Since engineering approaches to
systems biology are playing a significant rÃÂÃÂ´le in the rapid evolution of systems biology,
it is expected that mathematical optimization methods will contribute in a significant
way to advances in systems biology. Similarly, it is also expected that optimality
conditions will be useful to unravel the design principles of biological systems.
In this talk, I will highlight several topics where optimization has already made
significant contributions. Examples will be given where optimization methods are used
for topics ranging from model building and optimal experimental design to metabolic
engineering and synthetic biology. Finally, several perspectives for future research are
outlined. Flyer.

Apr 8th, 2008

Structural Modelling of the Whole Head for Electrical Impedance Imaging and Deep Brain Stimulation

Prof. Richard Bayford, Middlesex University and Imperial College

The use of deep brain stimulation (DBS) for clinical treatment for various neurological
disorders, particularly movement disorders such as Parkinson's disease is on the
increase. However, the mechanism by which this electrical stimulation acts on
neuronal activity is unclear. Experimental in situ investigation of the mechanism of
DBS in animal models or patients has clear limitations due to the multi-factorial
nature. Our aim is to produce an accurate computational model to simulate the
current flow produced by DBS. This will increase the understanding of the precise
effects of the injected current on the surrounding neural tissue. It will also allow us to
predict optimum dynamic injection and measurement protocols required to maximise
the effects of DBS in a defined region of the brain. To address this problem we have
adapted a bio imaging method known as electrical impedance tomography (EIT) for
DBS by extending the forward problem to create models of the whole human head to
simulate the dynamic electrical field distribution during deep brain stimulation. Flyer.

Feb 6th, 2008

Integrated stoichiometric, thermodynamic and kinetic modelling of
steady state metabolism

In the modelling of biochemical networks at steady state, equations
representing mass conservation, energy conservation, the second law of
thermodynamics and reversible enzyme kinetics can be formulated as a
single system of linear equalities and inequalities, in addition to
linear equalities on exponential variables.The reformulation is exact
and amenable to large scale nonlinear numerical analysis using linear
algebra, a prerequisite for computationally feasible genome scale
modelling. Integrating flux, concentration and kinetic parameters in
a unified constraint-based formulation is aimed at increasing the
quantitative predictive capacity of flux balance analysis.
Incorporation of experimental and theoretical bounds on thermodynamic
and kinetic variables ensures that steady state fluxes are both
thermodynamically and biochemically feasible. Preliminary numerical
results are demonstrated for a genome scale E.coli model with ~1600
metabolites and ~2300 fluxes. Connections between the current
approach and the mathematics of differential geometry and algebraic
geometry are highlighted. Flyer.

Nov. 14th, 2007

Seeing more than meets the eye

Richard Abadi, University of Manchester

Making sense of what we see relies on both data driven (bottom-up) and hypothesis
(top-down) processing. This latter stream uses internal models of the world and
expectation to bring awareness and plausible meaning to seeing. On occasions, when
direct analysis of the visual input becomes impaired by disease or trauma, higher
order processing can compensate by perceptually completing the missing input, and,
in cases of extensive visual loss, can also be responsible for the generation of visual
hallucinations. The occurrence of these possibilities offer useful opportunities to
explore and understand the neural underpinning of perception. This lecture will
describe some of our laboratory and clinical studies to characterise these phenomena.Flyer

Oct. 3rd, 2007

Mathematical modelling of cell signalling pathways: a useful tool for data integration and validation of hypothesis

Julio Vera, University of Rostock

In recent years, the analysis of cell signalling systems through data-based
models in ordinary differential equations (ODE) or other paradigms (e.g.
stochastic models) has emerged as an invaluable tool to understand the
underlying complexity of the protein interactions happening in cellular
signal transduction. Compared with other biochemical systems, the
modelling of cell signalling systems faces additional difficulties related to
the challenges quantifying protein-protein processes but also to the lack of
complete information about the topology of the considered network
interactions. Since in most of the metabolic systems the complete network
of interactions is (virtually) perfectly established, in cell signalling systems
the real structure of the pathways is an open question to be elucidated
either in parallel or through mathematical modelling based analysis.
In this context, the flexibility of kinetic models based on power-law
equations with non-integer kinetic orders has been validated in the recent
times as a tool to elucidate the structure of biochemical pathways via
quantitative data based modelling. In addition, recent investigations suggest
that this modelling framework could be a suitable tool for investigations on
the structure and systemic properties of cell signalling pathways.
In this talk we discuss the use of power-law models (advantages and
challenges) in biochemical systems. We also show how pre-existent
biological knowledge and quantitative data can be integrated through
mathematical modelling to validate hypothesis about the structure of
signalling pathways.
Flyer

Sep. 27th, 2007

Stochastic modelling of the immune response

Ken Duffy and Vijay Subramanian, Hamilton Institute, NUIM

During an adaptive immune response, lymphocytes proliferate for five to twenty cell
divisions, then stop and die over a period of weeks. The recently proposed Cyton
Model of lymphocyte proliferation provides a framework for studying this response.
Experimental evidence indicates that the fate of individual cells is potentially highly
variable. Thus the model assumes stochastic values for division and survival times for
each cell in a responding population.
In the paper that proposed the Cyton Model, the mathematical analysis used a direct
approach that enabled prediction of the mean immune response. Given the stochastic
nature of the model a more refined analysis is needed to determine the likelihood that
the typical response is close to the mean response. In this talk we present a more
sophisticated stochastic analysis of the system by introducing a generalisation to the
Bellman-Harris branching process. This enables us, for example, to determine
the expected variability in the immune response, which arises due to its cell-level
stochasticity.
We compare the predictions to experimentally observed lymphocyte population sizes
from experiments. The important biological conclusion that immune response is
typically robust and predictable despite the potential for great variability in the
experience of each individual cell.
We will assume as little probabilistic knowledge of the audience as possible.
Flyer

One important step in virtual drug design is the identification of new lead structures with respect to a pharmacological target molecule. The
search for new lead structures is often done with the help of a pharmacophore, which carries the essential structural as well as
physico-chemical properties that a molecule needs to have in order to bind to the target molecule. In the absence of the target molecule,
such a pharmacophore can be established by comparison of a set of active compounds. To identify their common features, a multiple
alignment of all or most of the active compounds is needed. Since the molecular shape plays a major role in the interaction between drug and
target, an alignment algorithm aiming at the elucidation of a pharmacophore should consider the molecule's `outer shape', which can
be approximated best by some kind of molecular surface.
This talk presents a new approach to molecular surface alignment which
is based on a discrete representation of shape as well as
physico-chemical properties using points. To distribute points
regularly on a molecular surface w.r.t. a smoothly varying point
density given on that surface, we developed a new point distribution
method based on centroidal Voronoi tesselation. For the computation of
pairwise surface alignments, we can then apply an efficient point
matching scheme, which we extended to surface points. Due to the
discrete representation of the molecules' shapes and properties,
multiple alignments can be computed from pairwise alignments in a
straight forward way. One hurdle that needs to be overcome, however,
is the large number of surface points that we consider. In this talk,
the application of the presented pairwise as well as multiple surface
alignment algorithms will be demonstrated on two sets of molecules: a
set of eight thermolysin inhibitors, and a set of seven HIV-1 protease
inhibitors.
Flyer

May 30th, 2007

Systems Biology at FCC -From Theory to Application

Henning Schmidt, Fraunhofer Chalmers Research Centre

The Bioinformatics and Systems Biology group at the Fraunhofer Chalmers Research Centre (FCC) provides an integrated approach to the study of biochemical and physiological processes, from the analysis of sequence data to the analysis of dynamic phenomena on a systems level. We develop mathematical methods and tools that aid to delineate and better understand the underlying cause of a disease or phenomenon at both the gene and mechanistic level, i.e., in terms of sequence data, quantitative data and its relation to biochemical reaction or interaction networks.
In this talk I will present work that I have been carrying out during the last years at FCC. The goal is to give an overview over past, recent, and planned activities, modeling projects, method and tool development.Flyer

May 15th, 2007

Applications of Probability in Genetics, Ecology and Population Genetics

John Moriarty, UCC

I will give an overview of the UCC Probability group's work at the interface between
probability, statistics and biology. This includes recent past work on circle covering
problems from genetics, current investigations in entropy estimation problems in
ecology, and planned future work in monte carlo methods in population genetics.
Flyer

There are many regulators of lymphocytes that alter the rates of proliferation and
affect differentiation, the change of cells from one type to another. In this talk I will
explore more closely how differentiation of lymphocytes is regulated by signals added
alone and together.
Key findings include:

Lymphocytes operate as if composed of a series of independent machines governing
times to divide, die and differentiate.

These machines are 'stochastic' making each cell slightly different.

Changes in the likelihood of differentiation are often linked to progressive cell
division.

Regulatory signals affect the mean and possibly the variance of the probability
distributions governing the internal machinery.

The extreme heterogeneity in fate of individual cells following stimulation of a
population can be described accurately by interleaving independent probability
distributions governing times to divide, die as well as the divisions at which
differentiation occurs.

Conflicting decisions of fate taken at the same time are often resolved with a
hierarchy of priority.

It is possible to incorporate these experimental rules into models that provide an
accurate, quantitative and internally regulable simulation of lymphocyte growth and
regulation.
Flyer

The evolution of HIV within individual patients is associated with disease
progression and failure of antiretroviral drug therapy. Using graphical
models we describe the development of HIV drug resistance mutations and
show how these models improve predictions of the clinical outcome of
combination therapy. We present combinatorial algorithms for computing the
risk of escape of an evolving population on a given fitness landscape.
The method is applied to calculating the likelihood of therapy failure as
a function of the viral genotype. Thus, it presents a step towards
personalized antiretroviral treatment.
Flyer

When lymphocytes, the primary mediators of immunity, are stimulated they proliferate
and their rate of growth, survival and differentiation is
highly regulated by the receipt of soluble and cell contact mediated
signals. This complex system is well suited to experimental dissection,
and offers a useful testing ground for developing concepts in systems
biology.
A major tool in measuring and analysing the immune response is flow
cytometry. Careful quantitative experiments with this method have
revealed how cells follow a combination of relatively simple cellular
rules operating independently. In this seminar I will discuss how these
rules can be used to develop quantitative models of cell growth and the
generation of cellular diversity that emerges during the immune response.
An important theme of my talk will be that intrinsic stochastic cellular
variability, easily dismissed as noise, may have evolved to be an
essential feature of immune regulation.
Flyer

Mar. 21st, 2007

Analysis of Metabolic Responses

Fernando Ortega, University of Birmingham.

Predicting the responses of intact cellular systems to environmental
and genetic changes has not been an easy task. Two of the major challenges
to understand metabolic responses are the structural complexity of the molecular
networks sustaining cellular functioning and the non-linearity inherent in the
interaction and kinetic laws involved. In the development of metabolic control
analysis (MCA), some strategies have been devised to deal with these difficulties.
Regarding network complexity, top-down or modular strategies have been proposed.
To deal with non-linearity two assumptions have been made. The first is that metabolic
perturbations and responses are small, so that they can be described using a first
order infinitesimal treatment. The second assumption is that in vivo enzyme catalysed
reaction rates are proportional to the corresponding enzyme concentrations.
However, many, if not most, of the responses exhibited by metabolic systems
subject to environmental changes or genetic manipulations involve large changes
in metabolic variables.To deal with this problem, we proposed an extension of
MCA that can be applied to arbitrarily large responses. Control and elasticity
coefficients for large changes are defined. These fulfil summation and connectivity
theorems, from which expressions for the control coefficients as a function of elasticity
coefficients (and the inverse design expressions) are obtained. In addition, the new
formalism can be applied in a top-down way to study the control of large metabolic
responses in intact cells. This will be exemplified with data reported in the literature.
Flyer

Feb. 21st, 2007

Modelling Environmental Fluctuations in Biochemical Systems

Andrea Rocco, University of Oxford.

Stochasticity is an essential ingredient of complex behaviours
in biological systems. I introduce a theoretical framework to
model environmental stochastic fluctuations in metabolic networks.
Non-trivial effects are predicted at both the kinetic and systemic
levels of description. In particular I propose the concept of
control by noise as a way of tuning the systemic behaviour of
metabolisms. This rests on a generalisation of standard Metabolic
Control Analysis when external fluctuations are considered,
which is based upon proper extensions of the Summation Theorems for
flux and concentration control coefficients. Finally I will discuss
some applications and plans for future research. Flyer

Dec. 6th, 2006

Duplication-Divergence and proteome evolution

Tim Rutjes

Current interest in biological interaction networks has focused on applications of graph theoretical tools to
real interaction data. One such application concerns the dynamics of proteome evolution. The proteome
evolution process is commonly described by a duplication-divergence (DD)-model. I will present and discuss
a recent DD-model. In particular, I will discuss the evolution of a large or giant connected component. An interesting
observation is that a component can split up during evolution. We investigated this theoretically and numerically.
I will also present some variations on the DD-model and outline some recommendations for future research.

Dec. 6th, 2006

Modelling mammalian cell culture proliferation

Thomas SchrÃÂÃÂ¶ck

Using experimental data from a joint project between UCD and NUIM, two mathematical models have been created:
(1) a simple growth kinetics model that uses a small set of ODEs; and (2) an age distributed cell cycle modelusing PDEs.
In this talk, I will review the biological background of cell culture experiments and cell behaviour. Different methods of
analysing experiment data will be discussed, some of which have direct applications in biological research. I present the
above mentioned models, along with methods to optimise a set of parameters.

Nov. 23rd, 2006

Systems Biology of Sponges: Understanding the evolution of integration in animals - New input from systems biology?

The research is motivated by the aim to understand how a living cell functions. Another motivation, more long term, is to assist the
pharmaceutical industry with rational drug design and to assist companies in biotechnology. This program is in the spirit of the systems
biology approach and it is executed in cooperations with biologists of the Vrije Universiteit (Hans Westerhoff, Barbara Bakker,
Frank Bruggeman, etc.). A biochemical reaction networks will be modelled as a positive control system. Dynamical system properties
and system theoretic properties will be described. The problem of rational drug design will be formulated as a control problem and an
approach will be discussed. The approach will be illustrated for glycolysis in Trypanosoma brucei. The problem of system reduction
is motivated by the very large size of dynamic systems obtained for realistic modeling of biochemical reaction networks in a cell. First
results and the approach to the research project for system reduction will be presented including the example of glycolysis in yeast.
System identification is another problem of biochemical reaction networks which requires attention and suggestions for an approach
will be presented. One aspect is the construction of observers which are used to obtain predictions of the state of a positive system.
The research program of the speaker for this area will be summarized and basic problems for positive systems will be mentioned.

March 24th, 2006

New 'Dimensions' in Genome Annotation

Prof. Bernhard Palsson, UC San Diego

June 17th, 2005

Validation of Biochemical Network Models using Robust Control Theory

Dr. Declan G. Bates, Control & Instrumentation Group University of Leicester, UK

E.T.S. Walton Lecture: Divide and conquer: The remarkable story of our
immune defense system

Dr. Phil Hodgkin, Walter and Eliza Hall Inst., Australia

Fifty years ago Macfarlane Burnet published a two page manuscript that came to be seen as one of the most important scientific papers of the 20th Century. In this paper Burnet outlined the principles for understanding immunity - the remarkable ability of our bodies to detect and eliminate a vast range of potential disease causing organisms. Burnet's idea solved a centuries old problem and precipitated a tremendous burst of experimental investigation into the detailed operation of the immune system that continues to this day and has had a major impact on human health.
In this anniversary year of Burnet's landmark paper, I will re-examine the problem of immune specificity and highlight the many medical triumphs that occurred both before and after BurnetÃÂ¢ÃÂÃÂs grand synthesis. In addition I will outline the state of immune knowledge today and look at some of the great challenges facing us if we are to further unlock the power of our immune systems for medical benefit. While the story of immunity illustrates the importance of scientific ideas and empirical discovery there is also a subplot. As a result of Burnet's ideas in the 1950s the small medical research community in Melbourne, where Burnet worked, became an international centre for immunology and established scientific traditions that continue strongly to the present day. Thus, remembering Burnet's paper in its anniversary year serves to illustrate the immense social and economic benefits that flow from supporting creative individuals and how their ideas continue to resonate within, and inspire, a community for many decades.